Data Driven Systems Biology: Harnessing Big Data and Systems Approaches to Decode Complex Biology

Title: From Omics to Mechanisms: Deep Learning Models of Molecular Networks for Precision Cancer Medicine

Bio: Avlant Nilsson is an Assistant Professor in Precision Medicine at the Department of Cell and Molecular Biology, Karolinska Institutet, and a group leader at SciLifeLab through the DDLS program. He holds an MSc (2014) and a PhD (2019) in Biological Engineering from Chalmers University of Technology, where his thesis focused on the metabolism of proliferating cells, including liver cancer. He then pursued postdoctoral research at the Massachusetts Institute of Technology (2019–2023), developing neural network models of signal transduction in immune cells. His research group, currently comprising of two PhD students and two postdoctoral researchers, develops data-driven models of molecular networks to understand how genetic alterations, cell type of origin, and cell–cell interactions shape cancer biology. The long-term goal of the lab is to advance computer-aided design of cancer medicine by predicting drug responses, resistance mechanisms, and microenvironmental interactions.

Abstract: TBA

Title: Learning cellular dynamics of tissues from single-cell and spatial omics

Bio: Jean researches mathematical rules in the molecular tricks that cancer cells use to escape destruction by immune cells. We seek to articulate the molecular chat between immune and cancer cells into equations, to serve as the foundation to engineer personalized cancer immunotherapy. We combine single-cell and spatial tumor profiling experiments, machine-learning & data science, and physics-style mathematical modeling.

Abstract: TBA

Title: Extracting protein-protein interactions from the literature with deep learning-based text mining

Bio: Katerina Nastou holds a Ph.D. in Bioinformatics and is a researcher at Statens Serum Institut in Copenhagen, specializing in multi-omics data analysis, biomedical text mining, and systems biology. Her work focuses on applying deep learning to extract and model molecular relationships from large-scale biological data and the scientific literature. She has contributed to the STRING database, a leading resource on protein networks, by upgrading its text-mining channel with advanced deep learning-based language models. She also currently collaborates internationally on projects such as AIM-HEART and EPOCH.

Abstract: TBA

Title: That’s Gonna Leave a Mark: Computational inference of complex cell features

Bio: Marcel studied Biology and Bioinformatics in Germany before starting his PhD in Computational Biology at Stockholm University. In the lab of Marc Friedländer he characterized subtle gene expression variations in virtually identical cells – linking them to regulatory layers and showing their predictive potential. He moved to the lab of Vicent Pelechano at Karolinska Institute for his postdoc to investigate single-cell RNA degradation dynamics and cell lineage relationships – resulting in pioneering work which showed that cellular ancestries can be predicted using only gene expression. In 2025, he started his lab as a DDLS fellow in precision medicine and diagnostics at Uppsala University and SciLifeLab, focusing on computational approaches to infer complex cell features, such as lineage and micro-environment, to characterize cancer heterogeneity and phenotype switches.

Abstract: TBA

Title: “Integrating protein interaction maps and omics for explainable health indicators”

Bio: Mika Gustafsson is a Professor in Translational Bioinformatics (PhD in Theoretical Physics, 2010) at the Department of Physics, Chemistry and Biology, Technical Faculty, Linköping University. Over the past ten years, he has led a research group of five to seven members. His core expertise lies in creating and integrating network analyses with omics and has been developing machine learning methods for precision medicine. In many projects, he has led medical doctors and molecular biologists in testing and validating omics-based findings, working primarily on complex diseases such as multiple sclerosis.

Abstract: TBA

Title: Dynamics of immunological tissue architecture linking inflammation with colorectal cancer

Bio: Simon Koplev is a SciLifeLab Fellow and newly appointed group leader in computational biology at KTH Royal Institute of Technology, Department of Gene Technology. He leads a computational biology research group investigating the fundamental principles and architecture of human tissues across organs in healthy steady-state and disease perturbations. The group is engaged with collaborative large-scale and open science efforts such as the Human Cell Atlas, developing the next generation of reference datasets and computational methods. Simon holds a PhD in Medical Science from the University of Cambridge at the Cancer Research UK Cambridge Institute supervised by John Marioni and Martin Miller. He did his postdoc with Sarah Teichmann at the Sanger Institute and Cambridge Stem Cell Institute, working on human single-cell and spatial studies of intestinal fibroblasts. Simon has 12 years of experience in bioinformatics research having published with more than 500 co-authors 35 peer-reviewed papers, spanning research on cancer, cardiovascular diseases, fibroblasts, gene regulatory networks, and computational methods development using machine learning. He holds a MScEng in Systems Biology from the Technical University of Denmark, supervised by Søren Brunak, including 2 semesters as a Research Scholar at the Dana-Farber Cancer Institute, Harvard Medical School. Simon began his scientific career with a BS in Biochemistry from the University of Copenhagen.

Abstract: TBA